英文翻译伍杰杰.doc

中南民族大学毕业论文(设计)英文翻译材料学院： 计算机科学学院 专业： 自动化 年级： 2007 学生姓名：伍杰杰 学号： 07064098 指导教师： 程立 职称：讲 师 2011年 05 月 05 日译文：数字IIR滤波器设计使用蚁群算法算法文献专业词汇DSP数字信号处理; recursive 递归; non-recursive非递归; FIR有限脉冲响应响应; IIR无限脉冲响应; multi-modal error surface多模态误差表面; SA现代算法; GA遗传算法; TS禁忌搜索; ACO蚁群算法; TACO蚁群优化算法; optimization优化; digital filter数字滤波器; adaptation process适应过程; the parameter space参数空间; the global minima全局最小值; probabilistic transition rules概率转换规则; a flexible memory可变内存; the global optimum solution全局优化方案; continuous optimization problems连续优化问题; objective function目标函数; a string of binary bits二进制位串; sub-path子路径; the intelligent problem人工智能问题; peaks峰值; memory内存 摘要: 改造和分析从模拟信号源采样而来的信号，应用信号处理（DSP）算法 。DSP的优点是基于这样的事实,即应用算法的性能一直是可预见的。没有依赖公差的电器元件作为模拟系统。DSP算法能合理地描述为一个数字滤波器。数字滤波器大体上分为两类:有限和无限的脉冲响应滤波器(检索)过滤器。因为错误的表面过滤器通常是信息检索的多式联运方式的要求,为了避免局部最小值和设计有效的数字信息检索过滤器。在这部作品中,提出了一种基于蚂蚁算法和能力提出了一种用于全球最佳化的数字信息检索滤波器的设计。仿真结果表明,本文提出的方法是准确和具有收敛速度快,计算结果表明,提出的方法可以有效地用于数字信息检索滤波器的设计。关键词: 蚁群;禁忌搜索;持续最优化改造和分析从模拟信号源采样而来的信号，应用信号处理（DSP）算法 。价格便宜，功能强大的通用计算机和定制设计的DSP芯片已经开发，DSP已经在通讯，生物医学，气象学和控制工程等几个非常重要的领域得以广泛应用。例如，DSP已经在储存和复制的音频和视频信号的领域广泛应用。 DSP的优势在于该算法的性能应用总是可以预测的，且不依赖于电子元件在模拟系统中的公差。任何DSP算法或处理器可以说是合理的数字滤波器。数字滤波器可大致分为两类：递归和非递归滤波器。非递归的，或者有限脉冲响应响应滤波器（FIR），只取决于现在的输入信号及其前任值。递归，或无限脉冲响应过滤器（IIR），不仅依赖于输入数据，而且还取决于一个或多个先前的输出值。IIR数字滤波器的主要优点是与拥有相同数量系数的FIR滤波器相比，它可以提供更好的性能。然而，关于IIR滤波器设计还存在一些问题。最根本的问题是，它们可能有一个多模态错误表面。另一个问题是过滤器在适应的过程可能变得不稳定。虽然这第二个问题可以很容易地通过限制参数空间处理，以避免第一个问题，一个可以实现在多模态误差全局最小的表面的设计方法是必需的。然而，传统的设计梯度搜索的方法可以很容易停留在表面的局部极小的误差。因此，一些研究人员试图发展诸如模拟全局优化设计算法的现代方法的算法（SA）和遗传算法（GA）。现代算法和遗传算法采用概率转换规则来搜寻错误表面的全球最低值。 遗传算法是一个人口为基础的算法（荷兰，1975年）和演进的解决方案，人口问题作为遗传算法试图改善单一的解决方案，使用一个邻域搜索机制（柯克帕特里克等，1983）。虽然遗传算法是很容易进行编程和地方衔接 ，这取决于最初的解决方案，它可能需要花费太多功能评估，以收敛到全局极小。通常发现遗传算法搜索到希望的地区十分迅速，但它往往需要太多的计算，从而达到局部极小概率转换规则，因为工作和邻域搜索机制不使用。这两种算法的缺点均不希望出现在IIR数字滤波器设计。另外两个具有全局优化能力的禁忌搜索（TS）和蚁群优化算法已被广泛采用的组合型问题启发式流行。格洛弗开发的TS算法是一种迭代搜索的形式，并根据智能解决问题的原则。TS有一个灵活的内存来保存有关搜寻的资料，过去的步骤，并使用它来创建和利用在搜索空间的新的解决方案。一个可能与传统的TS达到一个合理的计算时间时，最初的解决办法是远离所在地区存在的最优解全局最优解的问题。蚁群算法（ACO）模拟了真实蚁群的行为。该算法的主要特点是分布式计算，正反馈和反对构造性贪婪搜索。因此，蚁群算法的表现是由于本地搜索的积极反馈，由于分布计算全局搜索功能良好。在文献中，目前只有一对蚁群的不断优化和工程应用中提出的模型的几部作品。提出的旅游蚁群优化（南京塔塔汽车零部件）算法。在这项工作中，我们首先描述一个简单的策略对记忆功能的TS算法的基础，以提高南京塔塔汽车零部件非凸连续优化问题的一种表现，其次提出了一种新方法，南京塔塔汽车零部件为基础的数字IIR滤波器的设计。第2节描述了一个基本的蚁群算法，南京塔塔汽车零部件算法和提出的战略。第3节的测试功能和由南京塔塔汽车零部件的模拟结果和修改后的南京塔塔汽车零部件算法获得。第4节介绍了如何南京塔塔汽车零部件可应用于数字IIR滤波器的设计。并比较了TS，南京塔塔汽车零部件和修改后的南京塔塔汽车零部件的设计IIR滤波器算法的性能。蚁群算法是优化进行自然真实蚁群的过程人为的版本。在这种情况下，一个解决方案的目标函数值对应于一个真实的蚂蚁遵循的方式的长度。因此，由于信息素的金额存入一个自然的方式取决于它的长度，目标函数值可以被用来确定问题的解空间的人工信息素量的方法。因此，一个简单的原理图算法建模的主要步骤的真实蚁群的行为，可以概括如下：开始初始化重复为所有人工蚂蚁人工方式计算长度的所有人工方式更新信息素量就如何保持连接发现最近的路到现在直到结束。在南京塔塔汽车零部件算法描述Hiroyasu等。每个解决方案都代表了一个设计参数向量，其中每一个编码的二进制位串，即解决方案是一个二进制位向量。因此，人工蚂蚁为每个字符串中位值搜索，换句话说，他们试图决定是否位值是0或1。在为一个位的值决策阶段，蚂蚁只使用信息素的信息。一旦蚂蚁完成对字符串中所有值得决策过程，这意味着它已经产生了解决问题的方法。该解决方案进行评估的问题和一个数字值显示解决方案的质量是通过使用一个函数调用的评价函数来计算的。人工信息素是一种附着在子路径形成的解决方案是使用此值计算。毕竟在殖民地蚂蚁已经产生了解决方案和属于每个解决方案的信息素的金额已经被计算出来，分位之间的路径的信息素更新。这是进行信息素通过降低以前存入的金额和新的路径信息素量。假设被首选的子路径介于0和1（0-1）在一个阶段的概率计算。TS演算法是Glover为解决组合优化的困难问题在1986年提出来的 。TS是在传统计算机优化上，通过避免搜索空间中已经访问了点来增强局部搜索。事实上，它模拟人工智能的问题来解决在使用过程中的问题。TS算法的主要特点是它有一个明确的记忆，内存存储一个关于搜索过去步骤的信息，新举措会根据这个内存在一定的区域内产生。换句话说，搜索方向是受内存控制的。通过这种记忆方法，最近的举动在不在生产的这段时间内复制，因此这个搜索可以摆脱局部最小值，并找到带有多个峰的搜索空间中的一个。一个简单的TS采用两种存储器：频率和新近的回忆。频率的内存中存储的是如何经常的举动是在一个时间间隔，而新近产生的内存寄存器信息有关的时间（迭代）特定此举已最后一次尝试的信息。如果一个特定的移动频率超过预定期限，那么这将被列为禁忌。一动近因值等于现在之间的迭代，在哪个此举已尝试最后一次迭代的差异。正如频率一样，如果一动近因值超过预定限额，那么这一举动也列为禁忌。归类为禁忌的举动并没有再次尝试，直到他们得到的禁忌分类出来。使用这些记忆时，TS可以克服的单车周围局部极小的问题，并找到发生在一个多维，简称AE，下同搜索空间的全球最低。为了避免过早收敛问题遇到塔科的频率为基础的记忆体为基础的战略已经提出。在塔科的频率为基础的记忆存储有关的频率子路径是由蚁后的信息。虽然子路径的频率似乎是与子路径连接的信息素量一样，但实际上并不是这样的。通过检查的信息素量，很难断定是否大多数蚂蚁遵循一个子路径总价。但这是很容易通过评估频率信息的方法来实现。 原文：Designing digital IIR filters using ant colonyoptimisation algorithmAbstract: In order to transform and analyse signals that have been sampled from analogue sources, digital signal processing (DSP) algorithms are employed. The advantages of DSP are based on the fact that the performance of the applied algorithm is always predictable. There is no dependence on the tolerances of electrical components as in analogue systems. DSP algorithms can be reasonably described as a digital filter. Digital filters can be broadly divided into two-sub classes: finite impulse-response filters and infinite impulse-response (IIR) filters. Because the error surface of IIR filters is generally multi-modal, global optimisation techniques are required in order to avoid local minima and design efficient digital IIR filters. In this work, a new method based on the ant colonyoptimisation algorithm with global optimisation ability is proposed for digital IIR filter design. Simulation results show that the proposed approach is accurate and has a fast convergence rate, and the results obtained demonstrate that the proposed method can be efficiently used for digital IIR filter design. Keywords: Ant colony; Tabu search; Continuous optimisation In order to transform and analyse signals that have been sampled from analogue sources, digital signal processing (DSP) algorithms are employed. After the cheap and powerful general-purpose computers and custom-designed DSP chips have been developed, DSP has found very significant applications in several engineering areas from communication, biomedical, and control to meteorology. For example, DSP has obtained wide application in the storage and reproduction of audio and video signals. The advantages of DSP are based on the fact that the performance of the applied algorithm is always predictable. There is no dependence on the tolerances of electrical components as in analogue systems. Any DSP algorithm or processor can be reasonably described as a digital filter. Digital filters can be broadly classified into two groups: recursive and non-recursive filters. The response of non-recursive, or finite impulse-response (FIR) filters is dependent only upon present and previous values of the input signal. Recursive, or infinite impulse-response (IIR) filters, however, depend not only upon the input data but also upon one or more previous output values. The main advantage of a digital IIR filter is that it can provide a much better performance than the FIR filters having the same number of coefficients. However, there are some problems with the design of IIR filters. The fundamental problem is that they might have a multi-modal error surface. A further problem is the possibility of the filter becoming unstable during the adaptation process. Although this second problem can be easily handled by limiting the parameter space, in order to avoid the first problem, a design method which can achieve the global minima in a multi-modal error surface is required. However, the conventional design methods based on gradient search can easily be stuck at local minima of error surface. Therefore, some researchers have attempted to develop the design methods based on modern global optimisation algorithms such as the simulated annealing (SA) algorithm and genetic algorithm (GA) . SA and GA employ probabilistic transition rules to search the global minima in a error surface. GA is a population based algorithm (Holland, 1975) and evolves a population of solutions to the problem as SA attempts to improve a single solution using a neighbourhood search mechanism (Kirkpatrick et al., 1983). Although SA algorithm is quite easy to be programmed and good at local convergence, depending on the initial solution it might often require too many cost function evaluations to converge to the global minima. GA usually discovers the promising regions of search space very quickly, however it often needs too many computations to reach a local minima since the probabilistic transition rules are employed and a neighbourhood search mechanism is not used. These disadvantages of both algorithms are not desired in the design of digital IIR filters. Other two popular heuristics which have global optimisation ability are tabu search (TS) and ant colony optimisation algorithms which have been widely employed for combinatorial type problems. TS algorithm developed by Glover is a form of iterative search and based on intelligent problem solving principles. TS has a flexible memory to keep the information about the past steps of the search and uses it to create and exploit the new solutions in the search space. A conventional TS might have problem with reaching the global optimum solution in a reasonable computation time when the initial solution is far away from the region where optimum solution exists. Ant colony optimisation (ACO) algorithm simulates the behaviour of real ant colonies. The main features of the algorithm are distributed computation, positive feedback and con structive greedy search. Therefore, the performance of ACO algorithm is good for local search due to the positive feedback and for global search because of the distribution computation features. In the literature, there are just a few works on the models of ACO proposed for continuous optimization and their engineering applications .Hiroyasu have presented the touring ant colony optimisation (TACO) algorithm. In this work, we firstly describe a simple strategy based on the memory feature of TS algorithm to improve the performance of TACO algorithm for non-convex continuous optimization problems and secondly propose a new method based on TACO for digital IIR filter design. Section 2 describes a basic ACO algorithm, TACO algorithm and the proposed strategy. Section 3 presents the test functions and the simulation results obtained by TACO and the modified TACO algorithms. Section 4 describes how TACO can be applied to digital IIR filter design. It also compares the performance of TS, TACO and the modified TACO algorithms on IIR filter design. ACO algorithm is the artificial version of the natural optimisation process carried out by real ant colonies. If an optimisation problem can be expressed in the form of a minimisation problem a possible solution to this problem can be considered as a possible way between the nest and food in real ants world. In this case, the value of objective function for a solution corresponds to the length of the way followed by a real ant. Therefore, since the pheromone amount deposited on a natural way depends on its length, the objective function value can be used to determine the pheromone amount of artificial ways in the solution space of the problem. Hence, the main steps of a simple schematic algorithm modeling the behaviour of real ant colonies can be summarised as below: BEGIN Initialise REPEAT Generate artificial ways for all artificial ants Compute the length of all artificial ways Update the amount of pheromone attached on the ways Keep the shortest way found up to now UNTIL (maxiteration or a criteria is satisfied) END. In TACO algorithm described by Hiroyasu et al. (2000), each solution is represented by a vector of design parameters of which each is coded with a string of binary bits, i.e. a solution is a vector of binary bits. Therefore, artificial ants search for the value of each bit in the string, in other words, they try to decide whether the value of a bit is 0 or 1. At the decision stage for the value of a bit, ants use only the pheromone information. Once an ant completes the decision process for the values of all bits in the string, it means that it has produced a solution to the problem. This solution is evaluated for the problem and a numeric value showing the quality of the solution is calculated by using a function called the evaluation function. An artificial pheromone to be attached to the sub-paths forming the solution is computed using this value. After all ants in the colony have produced their solutions and the pheromone amount belonging to each solution has been calculated, the pheromones of sub- paths between the bits are updated. This is carried out by lowering the previous pheromone amounts and depositing the new pheromone amount on the paths. Assume that the probability of being preferred of the sub-path between 0 and 1 (0-1) at a stage is calculated. TS algorithm has been proposed by Glover in 1986 to solve difficult combinatorial optimisation problems. TS is a general heuristic for optimisation in conventional computers that enhance local search by attempting to avoid already visited points in the search space. In fact, it simulates the intelligent problem solving process used by human being. The main feature of TS algorithm is that it has an explicit memory. The memory stores an information about the past steps of search and new moves are produced in a certain neighbourhood according to this memory. In other words, the direction of search is controlled by the memory. By means of this memory, the moves produced recently are not reproduced within a period of time and hence the search can get out of a local minimum and find the global one of the search space with several peaks. A simple TS employs two kinds of memory: frequency and recency- based memories. The frequency-based memory stores an information about how often a move was produced during a time interval while the recency-based memory registers an information regarding the time (iteration) a specific move has been last time